openvinotoolkit · pj3iL · Dec 18, 2025 · Dec 18, 2025 · Dec 19, 2025
diff --git a/notebooks/3D-point-pillars/.dockerignore b/notebooks/3D-point-pillars/.dockerignore
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+# Git files
+.git
+.gitignore
+.gitattributes
+
+# Python cache
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+
+# Build artifacts
+build/
+dist/
+*.egg-info/
+.eggs/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Documentation
+*.md
+!README.md
+figures/
+misc/
+
+# Docker files
+devops/
diff --git a/notebooks/3D-point-pillars/.gitignore b/notebooks/3D-point-pillars/.gitignore
@@ -0,0 +1,8 @@
+__pycache__
+build/
+*.egg-info
+*.so
+*_logs*
+tmp/
+results/
+.idea
diff --git a/notebooks/3D-point-pillars/LICENSE b/notebooks/3D-point-pillars/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 ZhuLifa
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/notebooks/3D-point-pillars/README.md b/notebooks/3D-point-pillars/README.md
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+# Note
+
+_This directory is a fork from [PointPillars](https://github.com/zhulf0804/PointPillars) (commit 620e6b0)._
+-----------------------
+
+# [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://arxiv.org/abs/1812.05784)
+
+A Simple PointPillars PyTorch Implenmentation for 3D Lidar(KITTI) Detection. [[Zhihu](https://zhuanlan.zhihu.com/p/521277176)]
+
+- It can be run without installing [Spconv](https://github.com/traveller59/spconv), [mmdet](https://github.com/open-mmlab/mmdetection) or [mmdet3d](https://github.com/open-mmlab/mmdetection3d).
+- Only one detection network (PointPillars) was implemented in this repo, so the code may be more easy to read.
+- Sincere thanks for the great open-source architectures [mmcv](https://github.com/open-mmlab/mmcv), [mmdet](https://github.com/open-mmlab/mmdetection) and [mmdet3d](https://github.com/open-mmlab/mmdetection3d), which helps me to learn 3D detetion and implement this repo.
+
+## News
+
+- **2025-02** Making PointPillars a python package out of the code is supported.
+- **2024-04** Exporting PointPillars to ONNX & TensorRT is supported on branch [feature/deployment](https://github.com/zhulf0804/PointPillars/tree/feature/deployment).
+
+    ![](./figures/pytorch_trt.png)
+
+## mAP on KITTI validation set (Easy, Moderate, Hard)
+
+| Repo | Metric | Overall | Pedestrian | Cyclist | Car |
+| :---: | :---: | :---: | :---: | :---: | :---: |
+| this repo | 3D-BBox | 73.3259 62.7834 59.6278 | 51.4642 47.9446 43.8040 | 81.8677 63.6617 60.9126 | 86.6456 76.7439 74.1668 |
+| [mmdet3d v0.18.1](https://github.com/open-mmlab/mmdetection3d/tree/v0.18.1) | 3D-BBox  | 72.0537, 60.1114, 55.8320 | 52.0263, 46.4037, 42.4841 | 78.7231, 59.9526, 57.2489 | 85.4118, 73.9780, 67.7630 |
+| this repo | BEV | 77.8540 69.8003 66.6699 | 59.1687 54.3456 50.5023 | 84.4268 67.1409 63.7409 | 89.9664 87.9145 85.7664 |
+| [mmdet3d v0.18.1](https://github.com/open-mmlab/mmdetection3d/tree/v0.18.1) | BEV | 76.6485, 67.7609, 64.5605 | 59.0778, 53.3638, 48.4230 | 80.9328, 63.3447, 60.0618 | 89.9348, 86.5743, 85.1967 |
+| this repo | 2D-BBox | 80.5097 74.6120 71.4758 | 64.6249 61.4201 57.5965 | 86.2569 73.0828 70.1726 | 90.6471 89.3330 86.6583 |
+| [mmdet3d v0.18.1](https://github.com/open-mmlab/mmdetection3d/tree/v0.18.1) | 2D-BBox | 78.4938, 73.4781, 70.3613 | 62.2413, 58.9157, 55.3660 | 82.6460, 72.3547, 68.4669 | 90.5939, 89.1638, 87.2511 |
+| this repo | AOS | 74.9647 68.1712 65.2817 | 49.3777 46.7284 43.8352 | 85.0412 69.1024 66.2801 | 90.4752 88.6828 85.7298 |
+| [mmdet3d v0.18.1](https://github.com/open-mmlab/mmdetection3d/tree/v0.18.1) | AOS | 72.41, 66.23, 63.55 | 46.00, 43.22, 40.94 | 80.85, 67.20, 63.63 | 90.37, 88.27, 86.07 |
+
+- **Note: Here, we report [mmdet3d v0.18.1](https://github.com/open-mmlab/mmdetection3d/tree/v0.18.1) (2022/02/09-2022/03/01) performance based on the officially provided [checkpoint](https://github.com/open-mmlab/mmdetection3d/tree/v0.18.1/configs/pointpillars#kitti). Much improvements were made in the [mmdet3d v1.0.0rc1](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0rc1)**.
+
+## Detection Visualization
+
+![](./figures/pc_pred_000134.png)
+![](./figures/img_3dbbox_000134.png)
+
+## [Install]
+
+Install PointPillars as a python package and all its dependencies as follows:
+
+```
+cd PointPillars/
+pip install -r requirements.txt
+python setup.py build_ext --inplace
+pip install .
+```
+
+## [Datasets]
+
+1. Download
+
+    Download [point cloud](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_velodyne.zip)(29GB), [images](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_image_2.zip)(12 GB), [calibration files](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_calib.zip)(16 MB)和[labels](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_label_2.zip)(5 MB)。Format the datasets as follows:
+    ```
+    kitti
+        |- training
+            |- calib (#7481 .txt)
+            |- image_2 (#7481 .png)
+            |- label_2 (#7481 .txt)
+            |- velodyne (#7481 .bin)
+        |- testing
+            |- calib (#7518 .txt)
+            |- image_2 (#7518 .png)
+            |- velodyne (#7518 .bin)
+    ```
+
+2. Pre-process KITTI datasets First
+
+    ```
+    cd PointPillars/
+    python pre_process_kitti.py --data_root your_path_to_kitti
+    ```
+
+    Now, we have datasets as follows:
+    ```
+    kitti
+        |- training
+            |- calib (#7481 .txt)
+            |- image_2 (#7481 .png)
+            |- label_2 (#7481 .txt)
+            |- velodyne (#7481 .bin)
+            |- velodyne_reduced (#7481 .bin)
+        |- testing
+            |- calib (#7518 .txt)
+            |- image_2 (#7518 .png)
+            |- velodyne (#7518 .bin)
+            |- velodyne_reduced (#7518 .bin)
+        |- kitti_gt_database (# 19700 .bin)
+        |- kitti_infos_train.pkl
+        |- kitti_infos_val.pkl
+        |- kitti_infos_trainval.pkl
+        |- kitti_infos_test.pkl
+        |- kitti_dbinfos_train.pkl
+    ```
+
+## [Training]
+
+```
+cd PointPillars/
+python train.py --data_root your_path_to_kitti
+```
+
+## [Evaluation]
+
+```
+cd PointPillars/
+python evaluate.py --ckpt pretrained/epoch_160.pth --data_root your_path_to_kitti
+```
+
+## [Test]
+
+```
+cd PointPillars/
+
+# 1. infer and visualize point cloud detection
+python test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path
+
+# 2. infer and visualize point cloud detection and gound truth.
+python test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path --calib_path your_calib_path  --gt_path your_gt_path
+
+# 3. infer and visualize point cloud & image detection
+python test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path --calib_path your_calib_path --img_path your_img_path
+
+
+e.g.
+a. [infer on val set 000134]
+
+python test.py --ckpt pretrained/epoch_160.pth --pc_path pointpillars/dataset/demo_data/val/000134.bin
+
+or
+
+python test.py --ckpt pretrained/epoch_160.pth --pc_path pointpillars/dataset/demo_data/val/000134.bin \
+               --calib_path pointpillars/dataset/demo_data/val/000134.txt \
+               --img_path pointpillars/dataset/demo_data/val/000134.png \
+               --gt_path pointpillars/dataset/demo_data/val/000134_gt.txt
+
+b. [infer on test set 000002]
+
+python test.py --ckpt pretrained/epoch_160.pth --pc_path pointpillars/dataset/demo_data/test/000002.bin
+
+or
+
+python test.py --ckpt pretrained/epoch_160.pth --pc_path pointpillars/dataset/demo_data/test/000002.bin \
+               --calib_path pointpillars/dataset/demo_data/test/000002.txt \
+               --img_path pointpillars/dataset/demo_data/test/000002.png
+```
+
+## Acknowledements
+
+Thanks for the open source code [mmcv](https://github.com/open-mmlab/mmcv), [mmdet](https://github.com/open-mmlab/mmdetection) and [mmdet3d](https://github.com/open-mmlab/mmdetection3d).